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利用MDS2和SBOM数据进行LLM辅助的医疗器械漏洞分析

Stefan Stein¹, Michael Pilgermann¹, Simon Weber²

¹Department of Computer Science and Media, Brandenburg University of Applied Sciences, Magdeburger Straße 50, Brandenburg an der Havel, 14770, Brandenburg, Germany.

Computational and structural biotechnology journal

|July 31, 2025

概括

一个新的AI系统,VITAMedSec,使用提取增强生成 (RAG) 来分析医疗器械安全数据. 它自动识别漏洞并创建利用路径,增强网络安全威胁建模.

科学领域:

网络安全网络安全
人工智能的人工智能
医疗设备安全医疗设备安全

背景情况:

医疗器械安全对于患者安全至关重要.
分析设备漏洞的现有方法可能耗时.
需要对安全数据进行自动分析,以跟上不断变化的威胁.

研究的目的:

为了研究一个半自动的,基于Retrieval-Augmented Generation (RAG) 的多代理架构,用于分析医疗器械安全数据.
评估大型语言模型 (LLM) 驱动的代理系统的能力,以自主地将设备元数据与漏洞和漏洞相关联.
为了支持医疗设备的结构化威胁建模.

主要方法:

开发了一个半自动化的,基于RAG的多代理架构,名为VITAMedSec.
使用的医疗设备安全 (MDS2) 文件和软件材料清单 (SBOM) 作为输入的制造商披露声明.
通过公共漏洞数据库 (CVE,KEV,Metasploit) 增强数据.
采用静态RAG设计,预定义提示和固定的检索逻辑.

主要成果:

VITAMedSec系统成功地合成了可操作的见解和利用路径.
该系统在人类提示的监督下运行,在执行过程中没有手动的逐步输入.
在受控条件下获得了技术上一致的结果.

结论:

拟议的LLM驱动代理系统展示了自动化医疗器械威胁分析的潜力.
现实世界的验证是实际应用的关键下一步.
该框架具有双重用途的含义和广泛适用于关键基础设施安全,强调道德考虑.

关键词:

网络安全网络安全网络安全信息技术安全信息技术安全大型语言模型 (LLM)MDS2 是一个MDS2 系统.医疗器械安全性的制造商披露声明医疗网络物理系统医疗器械医疗器械医疗器械医疗信息系统医疗信息系统运营技术 (OT) 安全性安全性弱点弱点弱点弱点弱点

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